Each state consists of a n-dimensional observations vector from the environment and each observation is a scalar value called a feature. In other words, the observations in an n-dimensional observation space are feature vectors with n entries. In more complex tasks like image classification where the observation is a matrix rather than a vector, they can be flattened to vectors. Exact observations from the environment in some problems are not sufficient for representing states. As an example, assume a task with 2-dimensional observation space where the two dimensions are interrelated. In other words, possible actions for the situation that both dimensions are positive or negative are different from when they have different signs. In this case, using a vector with two entries as a state does not take any interaction between the two variables into account. Since, the observations in many environments are represented by numerical values, features can be interpolated to generate new meaningful features. One of the simplest families of features used for interpolation is Polynomial Features [1]}. Polynomial features are obtained by modeling the observations as any order-n polynomial. Formally speaking, assume \(O=(o_1,o_2,...,o_k)\)  is an observation vector from the environment. The new state \(s_i\)  corresponding to an observation \(o_i\)  is defined as:
\(s_i=\prod ^{k}_{j=1}{o_j^{c_{i,j}}}\) 
